VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech

• URL: http://arxiv.org/abs/2011.02314v1
• Date: Tue, 3 Nov 2020 08:49:33 GMT
• Title: VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech
• Authors: Kun Zhou, Berrak Sisman, Haizhou Li
• Abstract summary: We study the disentanglement and recomposition of emotional elements in speech through variational autoencoding Wasserstein generative adversarial network (VAW-GAN) We propose a speaker-dependent EVC framework that includes two VAW-GAN pipelines, one for spectrum conversion, and another for prosody conversion. Experiments validate the effectiveness of our proposed method in both objective and subjective evaluations.
• Score: 91.92456020841438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotional voice conversion (EVC) aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. In this paper, we study the disentanglement and recomposition of emotional elements in speech through variational autoencoding Wasserstein generative adversarial network (VAW-GAN). We propose a speaker-dependent EVC framework based on VAW-GAN, that includes two VAW-GAN pipelines, one for spectrum conversion, and another for prosody conversion. We train a spectral encoder that disentangles emotion and prosody (F0) information from spectral features; we also train a prosodic encoder that disentangles emotion modulation of prosody (affective prosody) from linguistic prosody. At run-time, the decoder of spectral VAW-GAN is conditioned on the output of prosodic VAW-GAN. The vocoder takes the converted spectral and prosodic features to generate the target emotional speech. Experiments validate the effectiveness of our proposed method in both objective and subjective evaluations.

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